import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
a  :      int            =    logging.get_logger(__name__)
def 		snake_case__	(		lowercase							,				lowercase					):
   lowerCAmelCase_:							str       =	[]
   for i in range(encoder_config.num_hidden_layers					):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (F'''encoder.deit.blocks.{i}.norm1.weight''', F'''encoder.encoder.layer.{i}.layernorm_before.weight''')					)
      rename_keys.append((F'''encoder.deit.blocks.{i}.norm1.bias''', F'''encoder.encoder.layer.{i}.layernorm_before.bias''')					)
      rename_keys.append(
          (F'''encoder.deit.blocks.{i}.attn.proj.weight''', F'''encoder.encoder.layer.{i}.attention.output.dense.weight''')					)
      rename_keys.append(
          (F'''encoder.deit.blocks.{i}.attn.proj.bias''', F'''encoder.encoder.layer.{i}.attention.output.dense.bias''')					)
      rename_keys.append(
          (F'''encoder.deit.blocks.{i}.norm2.weight''', F'''encoder.encoder.layer.{i}.layernorm_after.weight''')					)
      rename_keys.append((F'''encoder.deit.blocks.{i}.norm2.bias''', F'''encoder.encoder.layer.{i}.layernorm_after.bias''')					)
      rename_keys.append(
          (F'''encoder.deit.blocks.{i}.mlp.fc1.weight''', F'''encoder.encoder.layer.{i}.intermediate.dense.weight''')					)
      rename_keys.append(
          (F'''encoder.deit.blocks.{i}.mlp.fc1.bias''', F'''encoder.encoder.layer.{i}.intermediate.dense.bias''')					)
      rename_keys.append(
          (F'''encoder.deit.blocks.{i}.mlp.fc2.weight''', F'''encoder.encoder.layer.{i}.output.dense.weight''')					)
      rename_keys.append((F'''encoder.deit.blocks.{i}.mlp.fc2.bias''', F'''encoder.encoder.layer.{i}.output.dense.bias''')					)

   # cls token, position embeddings and patch embeddings of encoder
   rename_keys.extend(
       [
           ("encoder.deit.cls_token", "encoder.embeddings.cls_token"),
           ("encoder.deit.pos_embed", "encoder.embeddings.position_embeddings"),
           ("encoder.deit.patch_embed.proj.weight", "encoder.embeddings.patch_embeddings.projection.weight"),
           ("encoder.deit.patch_embed.proj.bias", "encoder.embeddings.patch_embeddings.projection.bias"),
           ("encoder.deit.norm.weight", "encoder.layernorm.weight"),
           ("encoder.deit.norm.bias", "encoder.layernorm.bias"),
       ]					)

   return rename_keys
def 		snake_case__	(		lowercase							,				lowercase					):
   for i in range(encoder_config.num_hidden_layers					):
      # queries, keys and values (only weights, no biases)
      lowerCAmelCase_:							Any       =	state_dict.pop(F'''encoder.deit.blocks.{i}.attn.qkv.weight'''					)

      lowerCAmelCase_:							Tuple       =	in_proj_weight[
          : encoder_config.hidden_size, :
      ]
      lowerCAmelCase_:							Optional[Any]       =	in_proj_weight[
          encoder_config.hidden_size : encoder_config.hidden_size * 2, :
      ]
      lowerCAmelCase_:							str       =	in_proj_weight[
          -encoder_config.hidden_size :, :
      ]
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							str       =	dct.pop(lowercase					)
   lowerCAmelCase_:							List[str]       =	val
def 		snake_case__	(		lowercase					):
   if "handwritten" in checkpoint_url:
      lowerCAmelCase_:							int       =	"https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg"  # industry
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
   elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
      lowerCAmelCase_:							Dict       =	"https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"
   lowerCAmelCase_:							List[str]       =	Image.open(requests.get(lowercase							,				stream=lowercase					).raw					).convert("RGB"					)
   return im
@torch.no_grad()
def 		snake_case__	(		lowercase							,				lowercase					):
   lowerCAmelCase_:							Any       =	ViTConfig(image_size=384							,				qkv_bias=lowercase					)
   lowerCAmelCase_:							Any       =	TrOCRConfig()

   # size of the architecture
   if "base" in checkpoint_url:
      lowerCAmelCase_:							List[str]       =	768
   elif "large" in checkpoint_url:
      # use ViT-large encoder
      lowerCAmelCase_:							Optional[Any]       =	1024
      lowerCAmelCase_:							Optional[Any]       =	4096
      lowerCAmelCase_:							int       =	24
      lowerCAmelCase_:							Tuple       =	16
      lowerCAmelCase_:							Tuple       =	1024
   else:
      raise ValueError("Should either find 'base' or 'large' in checkpoint URL"					)

   # the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
   if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
      lowerCAmelCase_:							Optional[Any]       =	False
      lowerCAmelCase_:							Any       =	"relu"
      lowerCAmelCase_:							Optional[Any]       =	1024
      lowerCAmelCase_:							Optional[Any]       =	True
      lowerCAmelCase_:							Any       =	False
      lowerCAmelCase_:							Optional[Any]       =	False

   # load HuggingFace model
   lowerCAmelCase_:							List[str]       =	ViTModel(lowercase							,				add_pooling_layer=lowercase					)
   lowerCAmelCase_:							Union[str, Any]       =	TrOCRForCausalLM(lowercase					)
   lowerCAmelCase_:							Optional[int]       =	VisionEncoderDecoderModel(encoder=lowercase							,				decoder=lowercase					)
   model.eval()

   # load state_dict of original model, rename some keys
   lowerCAmelCase_:							Tuple       =	torch.hub.load_state_dict_from_url(lowercase							,				map_location="cpu"							,				check_hash=lowercase					)["model"]

   lowerCAmelCase_:							List[str]       =	create_rename_keys(lowercase							,				lowercase					)
   for src, dest in rename_keys:
      rename_key(lowercase							,				lowercase							,				lowercase					)
   read_in_q_k_v(lowercase							,				lowercase					)

   # remove parameters we don't need
   del state_dict["encoder.deit.head.weight"]
   del state_dict["encoder.deit.head.bias"]
   del state_dict["decoder.version"]

   # add prefix to decoder keys
   for key, val in state_dict.copy().items():
      lowerCAmelCase_:							str       =	state_dict.pop(lowercase					)
      if key.startswith("decoder"					) and "output_projection" not in key:
         lowerCAmelCase_:							int       =	val
      else:
         lowerCAmelCase_:							List[str]       =	val

    # load state dict
   model.load_state_dict(lowercase					)

   # Check outputs on an image
   lowerCAmelCase_:							Optional[Any]       =	ViTImageProcessor(size=encoder_config.image_size					)
   lowerCAmelCase_:							Any       =	RobertaTokenizer.from_pretrained("roberta-large"					)
   lowerCAmelCase_:							Dict       =	TrOCRProcessor(lowercase							,				lowercase					)

   lowerCAmelCase_:							List[str]       =	processor(images=prepare_img(lowercase					)							,				return_tensors="pt"					).pixel_values

   # verify logits
   lowerCAmelCase_:							Union[str, Any]       =	torch.tensor([[model.config.decoder.decoder_start_token_id]]					)
   lowerCAmelCase_:							Dict       =	model(pixel_values=lowercase							,				decoder_input_ids=lowercase					)
   lowerCAmelCase_:							List[str]       =	outputs.logits

   lowerCAmelCase_:							Optional[int]       =	torch.Size([1, 1, 50265]					)
   if "trocr-base-handwritten" in checkpoint_url:
      lowerCAmelCase_:							str       =	torch.tensor(
          [-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]					)
   elif "trocr-large-handwritten" in checkpoint_url:
      lowerCAmelCase_:							List[Any]       =	torch.tensor(
          [-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170]					)
   elif "trocr-base-printed" in checkpoint_url:
      lowerCAmelCase_:							int       =	torch.tensor(
          [-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210]					)
   elif "trocr-large-printed" in checkpoint_url:
      lowerCAmelCase_:							Optional[Any]       =	torch.tensor(
          [-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535]					)

   if "stage1" not in checkpoint_url:
      assert logits.shape == expected_shape, "Shape of logits not as expected"
      assert torch.allclose(logits[0, 0, :10]							,				lowercase							,				atol=1E-3					), "First elements of logits not as expected"

   Path(lowercase					).mkdir(exist_ok=lowercase					)
   print(F'''Saving model to {pytorch_dump_folder_path}'''					)
   model.save_pretrained(lowercase					)
   print(F'''Saving processor to {pytorch_dump_folder_path}'''					)
   processor.save_pretrained(lowercase					)


if __name__ == "__main__":
 a  :      int            =    argparse.ArgumentParser()

 parser.add_argument(
     """--checkpoint_url""",
     default="""https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt""",
     type=str,
     help="""URL to the original PyTorch checkpoint (.pth file).""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
 )
 a  :      Any            =    parser.parse_args()
 convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
"""simple docstring"""



def 	_lowerCamelCase(	a				):
       return " ".join(
           "".join(word[::-1]				) if len(a				) > 4 else word for word in sentence.split()				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(reverse_long_words("""Hey wollef sroirraw"""))
